Leveraging Neural Network for Accurate and Context Aware Text Suggestions
Keywords:
Bidirectional, LSTM, Neural networks, Text suggestion, TokenizerAbstract
This project explores the use of Bidirectional Long Short Term Memory (Bi LSTM) neural networks to generate accurate and context aware text suggestions, aiming to overcome limitations in traditional rule based and shallow machine learning models. Using a dataset of over 6,500 text titles, the project applies tokenization, word embedding, and sequence padding to prepare data for deep learning.
Training was conducted on over 48,000 sequences using the Adam optimizer and categorical cross entropy loss function. The model demonstrated consistent improvements in prediction accuracy and loss reduction across training epochs. Over the course of training, the model demonstrated a significant improvement in accuracy and a steady reduction in loss, highlighting its ability to learn contextual patterns in the text data. Beyond technical performance, the project emphasizes ethical concerns such as bias and interpretability in NLP models, while also highlighting real world applications in predictive text systems, writing assistants, and automated content generation.
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